Machine Learning MCQ : Test 5

Explore this diverse selection of multiple-choice questions (MCQs) designed for various examinations. Machine Learning MCQ : Test 5 focuses on essential aspects of the subject, ensuring comprehensive preparation across different categories and fields of study to enhance your knowledge and readiness. The right answers for each question is provided next to respective questions for your convenience, you can either attend the test or dirtectly access the right answers by clicking the show correct answer button

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1. What is the purpose of a validation set in machine learning?

A. To train the model

B. To test the model

C. To tune model parameters

D. To increase data size

2. Which method is used for image segmentation?

A. Linear regression

B. K-means clustering

C. Convolutional neural network (CNN)

D. Random forest

3. What is the main advantage of using a recurrent neural network (RNN)?

A. Ability to handle variable-length input sequences

B. High computational efficiency

C. Simple architecture

D. Low memory usage

4. Which activation function can help mitigate the vanishing gradient problem?

A. Sigmoid

B. Tanh

C. ReLU (Rectified Linear Unit)

D. Linear

5. How does batch normalization improve training?

A. By normalizing the inputs to each layer

B. By increasing the learning rate

C. By reducing the model complexity

D. By clustering the data points

6. Which loss function is commonly used for binary classification?

A. Mean squared error

B. Cross-entropy loss

C. Hinge loss

D. L1 loss

7. How does a support vector machine (SVM) find the optimal hyperplane?

A. By minimizing the distance between data points

B. By maximizing the margin between classes

C. By clustering the data points

D. By reducing the number of features

8. What is the purpose of using a kernel function in SVM?

A. To reduce model complexity

B. To map data into higher-dimensional space

C. To cluster data points

D. To increase data size

9. Which technique is used for handling class imbalance?

A. Cross-validation

B. Feature scaling

C. Oversampling and undersampling

D. Dimensionality reduction

10. How does gradient boosting differ from AdaBoost?

A. Gradient boosting adds models sequentially to correct errors

B. AdaBoost uses bagging

C. Gradient boosting reduces model complexity

D. AdaBoost increases data size

11. What is a Boltzmann machine?

A. A type of clustering algorithm

B. A type of probabilistic graphical model

C. A type of linear regression

D. A type of support vector machine

12. Which method is used to handle missing data?

A. Cross-validation

B. Imputation

C. Feature scaling

D. Clustering

13. What is a common technique for feature selection?

A. K-means clustering

B. Principal component analysis (PCA)

C. Recursive feature elimination

D. Random forest

14. How does the k-nearest neighbors (k-NN) algorithm work?

A. By creating a decision tree

B. By finding the k closest data points and predicting the majority class

C. By reducing data dimensions

D. By increasing data size

15. Which type of machine learning algorithm is Q-learning?

A. Supervised learning

B. Unsupervised learning

C. Reinforcement learning

D. Semi-supervised learning

16. What is the role of a validation set?

A. To train the model

B. To test the model

C. To tune hyperparameters

D. To increase the data size

17. Which technique is used for object detection in images?

A. Linear regression

B. Convolutional neural networks (CNN)

C. K-means clustering

D. Random forest

18. How does the LSTM architecture address the vanishing gradient problem?

A. By using a simple architecture

B. By using gates to control the flow of information

C. By reducing the data size

D. By clustering the data points

19. What is the main purpose of transfer learning?

A. To increase data size

B. To leverage pre-trained models for new tasks

C. To reduce model complexity

D. To cluster data points

20. Which evaluation metric is used for multi-class classification?

A. Precision

B. Recall

C. F1 score

D. Accuracy

21. How does a gradient boosting machine (GBM) improve model performance?

A. By reducing data dimensions

B. By sequentially adding models to correct errors

C. By clustering data points

D. By increasing data size

22. What is the purpose of using a confusion matrix?

A. To reduce overfitting

B. To evaluate the performance of a classification model

C. To cluster data

D. To perform dimensionality reduction

23. Which type of learning algorithm is a generative adversarial network (GAN)?

A. Supervised learning

B. Unsupervised learning

C. Reinforcement learning

D. Semi-supervised learning

24. What is the purpose of the softmax function in a neural network?

A. To reduce data dimensions

B. To normalize outputs to a probability distribution

C. To cluster data points

D. To increase model complexity

25. How does the ROC curve help in evaluating a binary classifier?

A. By showing the trade-off between true positive rate and false positive rate

B. By clustering the data points

C. By reducing data size

D. By increasing model complexity

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